Audio systems often comprise at least one receiver, such as a microphone, for receiving environmental sound, a downstream signal processing unit, such as an amplifier, and at least one audio output device, such as a loudspeaker. A hearing instrument belongs to such an audio system.
FIG. 1 illustrates the basic structure of a typical hearing instrument. The hearing instrument comprises a housing 1. Inside the housing 1, there are microphones 2, a signal processing unit 3, a loudspeaker 4, and a battery 5. The signal processing unit 3 includes signal input ports 6 and a signal output port 7. The signal input ports 6 are connected with microphones 2; and the signal output port 7 is connected with the loudspeaker 4. The battery 5 is connected to the signal processing unit 3 to supply power. The microphones 2 receive environmental sound and output a microphone signal. Subsequently, the microphone signal gets processed by the signal processing unit 3. The loudspeaker 4 converts the processed signal output from the signal processing unit 3 into sound.
A major problem of such audio systems is feedback whistling. The sound from the loudspeaker can reenter the audio system through the microphone, get further amplified, and then be emitted through the loudspeaker. In this situation, a closed loop (microphone->amplifier->loudspeaker->microphone, and so on) is built up, and feedback whistling occurs when the total system gain exceeds a certain threshold.
Feedback whistling can be reduced or even eliminated by feed-back cancellers. A known feedback canceller is an adaptive feedback compensator, which relies on an adaptive filter that models the time-variant acoustic feedback path transfer function g.
A common example of an adaptation rule for the update of the adaptive filter coefficients h is the normalized least mean square (NLMS) algorithm:h(k+1)=h(k)+p[(e*(k)x(k))/(x*(k)x(k))].
In this formula, k represents the discrete time index, x is the input to the adaptive feedback compensator, e=m−c is the error signal defined as the difference of the microphone signal m and the feedback compensation signal c, p represents the step-size parameter that controls the adaptation speed of the adaptive filter, and * denotes the conjugate complex operation.
A block circuit diagram of an audio system with an adaptive feedback compensator is shown in FIG. 2. Here, the time de-pendency (discrete time index k) is omitted for simplification. The feedback sound f from an audio output (e.g. loud-speaker 13) is fed back via the acoustic feedback path 15 to the receiver (e.g. microphone 10). This acoustic feedback path 15 is equipped with a transfer function g. In addition to the feedback sound f, the microphone 10 also picks up a required sound s which is a useful environmental sound required to be amplified, and outputs a microphone signal m.
The microphone signal m is subsequently processed by a signal processing device 11, which is located between the microphone and the loudspeaker 13. The signal processing device 11 comprises a core signal processing unit 12 (SP), a feedback compensator 14 (FBC), and a subtractor. The feedback compensator 14 outputs a feedback compensation signal c, which is subtracted from the microphone signal m in the subtractor, so an error signal e=m−c is obtained. The error signal e further enters the core signal processing unit 12 and outputs a signal x. The output signal x is fed to the loudspeaker 13 and to the feedback compensator 14. The feedback compensator 14 employs a transfer function h, which estimates the acoustic feedback path transfer function g. Therefore, the feedback compensation signal c, which is generated from the feedback compensator 14, reads c=h*x. In addition, the feedback compensator 14 is also controlled by the error signal e.
More details are given in recent literature, such as: S. Hay-kin, Adaptive Filter Theory. Englewood Cliffs, N.J.: Prentice-Hall, 1996; and Toon van Waterschoot and Marc Moonen, “Fifty years of acoustic feedback control: state of the art and future challenges”, Proc. IEEE, vol. 99, no. 2, February 2011, pp 288-327.
As mentioned before, the parameter p, also called the stepsize parameter, controls the adaptation speed of an adaptive filter. A proper, time-dependent controlling of p is demanded for an effective and stable feedback canceller behavior: If p is large, the adaptive filter quickly adapts to situation changes of the acoustic feedback path 15 and thus prevents whistling. On the other side, if p is permanently too high, erroneous adaptations for strongly correlated excitation signals (e.g. music, the clinking of glass or cutlery) may arise.
Previously, feedback detectors were used to control the step-size p in a way that the step-size p will be increased if feedback is detected and reduced if no feedback is detected. With this method, it is possible to eliminate feedback very efficiently and to avoid artifacts.
The conventional method above has the following two problems to be dealt with:
1) An initiation by whistling is always needed. In other words, it is not possible to detect feedback before whistling starts. The reason is that if the feedback path changes fast, whistling will start after a short time.
2) The amount of artifacts is dependent on the quality of feedback detections. Misdetections can cause a large amount of artifacts. The main challenge here is to distinguish between feedback whistling and required signals from the environment like flutes, bells and all other sounds, which are strongly correlated excitation signals and, in the worst ca-se, sinusoidal. Here, a required signal is a signal that is needed or at least desirable to be provided for authentic representation of the respective sound environment.
Therefore, properly controlling step-size parameter p is crucial to aim feedback reduction systems. There exist several concepts for suitably controlling step-size parameter p in the prior art. However, all of them require making compromises. In the following, two prior art concepts are presented:
1. Optimal step-size estimate. Taking into account some simplifications and assumptions, a theoretical optimum step-size p can be derived aspopt=E{IcI}/E{IeI}where E{ } denotes the expectation operator.
The derived formula helps to stabilize the adaptation, but does not solve the problem. In practice, the assumptions made for the derivation (e.g. assuming uncorrelated receiver and microphone signals, assuming the unknown acoustic feedback path transfer function to be zero), are often not fulfilled, leading to a deterioration of the performance.
2. A triggered adaptation by feedback detectors. The scenario is following: In normal mode the adaptation is frozen via a small value of p. The adaptation is only allowed if a feed-back detector gets active, indicating a change in the acoustic feedback path. As a consequence, the need for a re-adaptation of the feedback compensator transfer function h is triggered by a temporal increase of the step-size parameter p.
On one hand, freezing the adaptation guarantees stability (i.e. no erroneous adaptation for strongly correlated excitation signals), as long as no feedback detector gets erroneously active. On the other hand, the feedback canceller adapts only if the feedback detector gets active. In everyday life, it may happen quite often that the acoustic feedback path changes only slightly (e.g. passing a door, sitting down on a couch), which provokes no feedback detection. As there is no adaptation, the state of the feedback compensator transfer function h does not represent the current acoustic feedback path, resulting in an audible rough sound quality. The reduced sound quality will last on until a feedback detector gets active, which is usually accompanied with a feedback whistling. As a countermeasure, the sensitivity of the feedback detector could be adjusted. Therefore, minimal changes of the acoustic feedback path are detected, but at the cost of increased erroneous feedback detections leading to increased artifacts.
Both of the above methods for controlling p can be used together. Nevertheless, the user has to live with a compromise: He either accepts a rough sound quality for non-detected path changes or puts up with false feedback detections incurring the risk of erroneous adaptations that leads to tonal disturbances or other processing artifacts.